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# Conflicts: # python_for_data_science.pptx
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docs/README.md

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If you plan to do Python programming in a Linux or HPC environment you should
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be familiar with these as well.
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For following along hands-on, you need
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* laptop or desktop with internet access.
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* a system set up so you can connect to an HPC system, an account on an HPC
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system (e.g., VSC, CECI, ...), compute credits if that is required to run
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jobs on the HPC system if you want to use an HPC system;
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* a Python environment that can run Jupyter Lab if you want to use your own system;
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* access to Google Colaboratory if you prefer not to install software.
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## Level
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* Introductory: 30 %
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* Intermediate: 50 %
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* Advanced: 20 %
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## Trainer(s)
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docs/_config.yml

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theme: jekyll-theme-slate
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title: "Python for data science"
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theme: jekyll-theme-slate

source-code/README.md

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representation and algorithms.
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* [`pandas`](pandas): illustrations of using pandas and seaborn.
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* [`polars`](polars): Kllustrations of using polars.
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* [`duckdb`](duckdb): illustrations of using DuckDB for SQL queries.
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* [`regexes`](regexes): illustrations of using regular expressions for
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validation and information extraction from textual data.
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* [`seaborn`](seaborn): illustrations of using Seaborn to create plots.

source-code/duckdb/README.md

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# DuckDB
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DuckDB is an in-process SQL OLAP database management system. It is designed to
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support analytical query workloads and is optimized for fast query performance
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on large datasets. DuckDB can be embedded directly into applications, making it
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a popular choice for data analysis tasks in various programming environments.
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## What is it?
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1. `patients.ipynb`: A Jupyter notebook that demonstrates how to use DuckDB for
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analyzing patient data. It includes examples of loading data and executing
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SQL queries.
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1. `data/`: CSV files to use with the notebook.
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,patient,dose,date,temperature
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0,1,0.0,2012-10-02 10:00:00,38.3
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,patient,gender,condition
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0,1,M,A
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2,3,M,A
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3,5,M,A
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4,6,F,B
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